目的:鉴定卵巢癌(OC)的潜在诊断标志物,探讨免疫细胞浸润在OC发病机制中的作用。方法:作为研究队列,将取自基因表达综合(GEO)数据库的人类OC的两个基因表达数据集(GSE27651和GSE26712,作为元数据)组合起来,包括228个OC和16个对照样品。进行分析以鉴定OC和对照样品之间的差异表达基因。同时使用递归特征消除算法和最小绝对收缩和选择算子回归进行支持向量机分析,以识别可以区分OC的候选生物标志物。此外,进行免疫组织化学染色以验证候选生物标志物的诊断价值和蛋白质表达水平。GSE146553数据集(0Cn=40,对照n=3)用于进一步验证这些生物标志物的诊断值。Further,使用CIBERSORT算法评估OC和对照样品中各种免疫细胞浸润的比例.结果:CLEC4M,PFKP,在元数据(受试者工作特征曲线下面积[AUC]=0.996,AUC=1.000,AUC=1.000)和GSE146553数据集(AUC=0.983,AUC=0.975,AUC=0.892)中,SCRIB被鉴定为OC的潜在诊断标志物。关于免疫细胞浸润,滤泡辅助树突状细胞的浸润增加,M2巨噬细胞和中性粒细胞的浸润减少,以及OC中激活的自然杀伤(NK)细胞和T细胞。CLEC4M与中性粒细胞(r=0.57,p<0.001)和静息NK细胞(r=0.42,p=0.0047)呈显著正相关,但与活化的树突状细胞呈负相关(r=-0.33,p=0.032)。PFKP与活化NK细胞(r=0.36,p=0.016)和滤泡辅助性T细胞(r=0.32,p=0.035)呈显著正相关,但与幼稚B细胞(r=-0.3,p=0.049)和静息NK细胞(r=-0.41,p=0.007)呈负相关。SCRIB与浆细胞呈显著正相关(r=0.39,p=0.01),记忆B细胞(r=0.34,p=0.025),和滤泡辅助性T细胞(r=0.31,p=0.04),但与中性粒细胞(r=-0.46,p=0.002)和幼稚B细胞(r=-0.48,p=0.0012)呈负相关。结论:CLEC4M,PFKP,和SCRIB被鉴定并验证为OC的潜在诊断生物标志物。这项工作和对这三种生物标志物的鉴定可能为将来对OC的机制和治疗的研究提供指导。
Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the
CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.